Granger causality

tests/TestGranger.py

@@ -12,14 +12,26 @@
 
 from causallearn.search.Granger.Granger import Granger
 
+######################################### Test Notes ###########################################
+# All the benchmark results (p_value_matrix_truth, adj_matrix_truth, coeff_truth)              #
+# are obtained from the code of causal-learn as of commit                                      #
+# https://github.com/cmu-phil/causal-learn/commit/b49980d046607baaaa66ff8dc0ceb98452ab8616     #
+# (b49980d).                                                                                   #
+#                                                                                              #
+# We are not sure if the results are completely "correct" (reflect ground truth graph) or not. #
+# So if you find your tests failed, it means that your modified code is logically inconsistent #
+# with the code as of b49980d, but not necessarily means that your code is "wrong".            #
+# If you are sure that your modification is "correct" (e.g. fixed some bugs in b49980d),       #
+# please report it to us. We will then modify these benchmark results accordingly. Thanks :)   #
+######################################### Test Notes ###########################################
+
 
 class TestGranger(unittest.TestCase):
     # simulate data from a VAR model
     def syn_data_3d(self):
         # generate transition matrix, time lag 2
         np.random.seed(0)
         A = 0.2 * np.random.rand(3,6)
-        print('True matrix is \n {}'.format(A))
         # generate time series
         T = 1000
         data = np.random.rand(3, T)
@@ -35,7 +47,6 @@ def syn_data_2d(self):
         A = 0.5*np.random.rand(2,4)
         A[0,1] = 0
         A[0,3] = 0
-        print('True matrix is \n {}'.format(A))
         # generate time series
         T = 100
         data = np.random.rand(2, T)
@@ -54,8 +65,10 @@ def test_granger_test(self):
         dataset = self.syn_data_2d()
         G = Granger()
         p_value_matrix, adj_matrix = G.granger_test_2d(data=dataset)
-        print('P-value matrix is \n {}'.format(p_value_matrix))
-        print('Adjacency matrix is \n {}'.format(adj_matrix))
+        p_value_matrix_truth = np.array([[0, 0.5989, 0, 0.5397], [0.0006, 0, 0.0014, 0]])
+        adj_matrix_truth = np.array([[1, 0, 1, 0], [1, 1, 1, 1]])
+        self.assertEqual((np.round(p_value_matrix, 4) - p_value_matrix_truth).all(), 0)
+        self.assertEqual((adj_matrix - adj_matrix_truth).all(), 0)
 
     # example2
     # for data with multi-dimensional variables, granger lasso regression.
@@ -66,10 +79,8 @@ def test_granger_lasso(self):
         dataset = self.syn_data_3d()
         G = Granger()
         coeff = G.granger_lasso(data=dataset)
-        print('Estimated matrix is \n {}'.format(coeff))
-
+        coeff_truth = np.array([[0.09, 0.1101, 0.1527, 0.1127, 0.0226, 0.1538],
+                                [0.1004, 0.15, 0.1757, 0.1037, 0.1612, 0.0987],
+                                [0.1155, 0.1485, 0, 0.039, -0., 0.1085]])
+        self.assertEqual((np.round(coeff, 4) - coeff_truth).all(), 0)
 
-if __name__ == '__main__':
-    test = TestGranger()
-    test.test_granger_test()
-    test.test_granger_lasso()